In this study, we report a corpus analysis of rock harmony. As a corpus, we used Rolling Stone magazine's list of the ‘500 Greatest Songs of All Time’; we took the 20 top-ranked songs from each decade (the 1950s through the 1990s), creating a set of 100 songs. Both authors analysed all 100 songs by hand, using conventional Roman numeral symbols. Agreement between the two sets of analyses was over 90 per cent. The analyses were encoded using a recursive notation, similar to a context-free grammar, allowing repeating sections to be encoded succinctly. The aggregate data was then subjected to a variety of statistical analyses. We examined the frequency of different chords and chord transitions. The results showed that IV is the most common chord after I and is especially common preceding the tonic. Other results concern the frequency of different root motions, patterns of co-occurrence between chords, and changes in harmonic practice across time.

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The prevailing approach to bar lengths in pop/rock music uses the standard rock drum beat as a model, whereby the kick is assigned to beats 1 and 3 and the snare to beats 2 and 4 in a bar of ".fn_meter(4,4).". In this paper, I show that a song’s drum pattern is not a reliable indicator of measure lengths, especially if we consider bar lengths to be an important benchmark for theories of form. I argue that our determinations of bar lengths and meter in popular music should also take absolute time into consideration. Specifically, I speculate that the two-second measure acts as an ideal for experiential or “real” measures, and so we may be best served—all other factors being equal—by partitioning a song into measure lengths that most closely approximate two seconds. My approach derives from recent research on tempo perception, statistical studies of pop/rock song corpora, and my own analyses of popular songs. An important concept is the notion of different drum “feels,” such as double-time and half-time, in which the drum pattern can be seen to exist on a metric level above or below the primary beat level as implied by the time signature. I show the value of my approach via a number of song comparisons, wherein structural similarities can be found despite differences in surface-level rhythmic patterns. I also discuss other factors—including harmonic rhythm and form—that may affect our perception of bar lengths, so as to concede that no single factor can fully simplify meter classification in this style.